/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    RandomizableParallelIteratedSingleClassifierEnhancer.java
 *    Copyright (C) 2009-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Vector;

import weka.core.Option;
import weka.core.Randomizable;
import weka.core.Utils;

/**
 * Abstract utility class for handling settings common to randomizable meta
 * classifiers that build an ensemble in parallel from a single base learner.
 *
 * @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
 * @version $Revision$
 */
public abstract class RandomizableParallelIteratedSingleClassifierEnhancer extends ParallelIteratedSingleClassifierEnhancer implements Randomizable {

    /**
     * For serialization
     */
    private static final long serialVersionUID = 1298141000373615374L;

    /** The random number seed. */
    protected int m_Seed = 1;

    /**
     * Returns an enumeration describing the available options.
     *
     * @return an enumeration of all the available options.
     */
    public Enumeration<Option> listOptions() {

        Vector<Option> newVector = new Vector<Option>(1);

        newVector.addElement(new Option("\tRandom number seed.\n" + "\t(default 1)", "S", 1, "-S <num>"));

        newVector.addAll(Collections.list(super.listOptions()));
        return newVector.elements();
    }

    /**
     * Parses a given list of options. Valid options are:
     * <p>
     *
     * -W classname <br>
     * Specify the full class name of the base learner.
     * <p>
     *
     * -I num <br>
     * Set the number of iterations (default 10).
     * <p>
     *
     * -S num <br>
     * Set the random number seed (default 1).
     * <p>
     *
     * Options after -- are passed to the designated classifier.
     * <p>
     *
     * @param options the list of options as an array of strings
     * @exception Exception if an option is not supported
     */
    public void setOptions(String[] options) throws Exception {

        String seed = Utils.getOption('S', options);
        if (seed.length() != 0) {
            setSeed(Integer.parseInt(seed));
        } else {
            setSeed(1);
        }

        super.setOptions(options);
    }

    /**
     * Gets the current settings of the classifier.
     *
     * @return an array of strings suitable for passing to setOptions
     */
    public String[] getOptions() {

        Vector<String> options = new Vector<String>();

        options.add("-S");
        options.add("" + getSeed());

        Collections.addAll(options, super.getOptions());

        return options.toArray(new String[0]);
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String seedTipText() {
        return "The random number seed to be used.";
    }

    /**
     * Set the seed for random number generation.
     *
     * @param seed the seed
     */
    public void setSeed(int seed) {

        m_Seed = seed;
    }

    /**
     * Gets the seed for the random number generations
     *
     * @return the seed for the random number generation
     */
    public int getSeed() {

        return m_Seed;
    }

}
